Abstract: This study presents a novel method to detect and classify power quality disturbances using wavelets. The proposed algorithm uses different wavelets ...
American Journal of Applied Sciences 3 (10): 2049-2053, 2006 ISSN 1546-9239 © 2006 Science Publications
A Novel Detection and Classification Algorithm for Power Quality Disturbances using Wavelets 1
C. Sharmeela, 2M.R. Mohan, 2G.Uma and 2J.Baskaran 1 A.C. College of Technology, Anna University 2 DEEE, College of Engineering, Anna University, Chennai-600 025, India Abstract: This study presents a novel method to detect and classify power quality disturbances using wavelets. The proposed algorithm uses different wavelets each for a particular class of disturbance. The method uses wavelet filter banks in an effective way and does multiple filtering to detect the disturbances. A qualitative comparison of results shows the advantages and drawbacks of each wavelet when applied to the detection of the disturbances. This method is tested for a large class of test conditions simulated in MATLAB. Power quality monitoring together with the ability of the proposed algorithm to classify the disturbances will be a powerful tool for the power system engineers. Key words: Wavelet transforms, power quality disturbances, transients, voltage sag, voltage swell INTRODUCTION Electric power quality is an important issue in power systems nowadays .The demand for clean power has been increasing in the past several years. The reason is mainly due to the increased use of microelectronic processors in various types of equipments such as computer terminals, programmable logic controllers and diagnostic systems. Most of these systems are quite susceptible to disturbances in the supply voltage. For example a momentary power interruption or thirty percent voltage sag lasting for hundredth of a second can reset the PLCs in an assembly line. The amount of waveform distortion has been found to be more significant nowadays due to the wide applications of nonlinear electronic devices in power apparatus and systems. Without determining the existing levels of power quality, electric utilities cannot adopt suitable strategies to provide a better service. Therefore an efficient approach of justifying these electric power quality disturbances is motivated. Several research studies regarding the power quality have been conducted. Their aims were often concentrated on the collection of raw data for a further analysis, so that the impacts of various disturbances can be investigated. Sources of such disturbances can be located or further mitigated. However, the amount of acquisition data was often massive in their test cases. Such an abundance of data may be time consuming for the inspection of possible culprits. A more efficient approach is thus required in the power quality assessment. The implementation of the discrete Fourier transform by various algorithms has been constructed as the basis of modern spectral analysis. Such transforms were successfully applied to stationary Corresponding Author:
signals where the properties of signals did not evolve in time. However, for those non-stationary signals any abrupt change may spread over the whole frequency axis. In this situation, the Fourier transform is less efficient in tracking the signal dynamics[1]. A point -topoint comparison scheme has been proposed to discover the dissimilarities between consecutive cycles[2]. This approach was feasible in detecting certain kinds of disturbances but fail to detect those disturbances that appear periodically. With the introduction of new network topologies and improved training algorithms, neural network technologies have demonstrated their effectiveness in several power system applications[3]. Once the networks have been well trained, the disturbances that correspond to the new scenario can be identified in a very short time[4]. This technique has also been applied in the power system applications. However, it can only be applied to detect a particular type of disturbance. When encountering different disturbances, the network structure has to be reorganized, plus the training process must be restarted. A method of detecting power quality disturbances based on neural networks and wavelets has been proposed[7]. In this method, the fundamental component is removed using wavelets and the remaining signal corresponding to disturbances is processed and given as input to ANN. However, this method fails to detect voltage sag/swell and also new ANN’s have to be developed for different rated load voltages and sampling frequencies. Recently with the emergence of wavelets it has paved a unified framework for signal processing and its applications[5]. Fourier transforms rely on a uniform window for spreaded frequencies. Wavelet transforms can apply various lengths of windows according to the amount of signal frequencies. Characteristics of non-
C. Sharmeela, A.C. College of Technology, Anna University, DEEE, College of Engineering, Anna University, Chennai-600 025, India
2049
Am. J. Appl. Sci., 3 (10): 2049-2053, 2006 stationary disturbances were found to be more closely monitored by wavelets. The transient behavior, cavities and discontinuities of signals can be all investigated by wavelet transforms. For example, if there is an instantaneous impulse disturbance, which happens at a certain time interval it may contribute to the Fourier transform, but its location on the time axis is lost. However, by wavelets both time and frequency information can be obtained. In other words, the wavelet transform are more local. Instead of transforming a pure ‘time domain’ in to a pure’ frequency domain ’, the wavelet transforms find a good compromise in time - frequency domain. This paper presents novel algorithm, which overcomes all these difficulties and can accurately detect and classify the disturbances present in the signal. This method is independent of the load voltage and can be easily customized for different sampling frequencies. In this approach, for detecting each disturbance a particular wavelet is used. The method uses wavelet filter banks in an effective way and does multiple filtering to detect the disturbances. The performance evaluation of different wavelets in the proposed method shows the capability of a particular wavelet in detecting a particular disturbance.
START Get input disturbance data Initialise i = 1, d = 1, ST(k) =0
Is i
